From Machine Learning to Artificial Intelligence

How humans and machines work together to stop attacks

Combining the unique strengths of humans and machines for better security outcomes

Today’s security landscape is changing very fast. The number of cyberattacks each day has risen from a mere 500 to an estimated 200,000-500,000. The volume of threats and information that must be processed is greater than humans alone can manage. We need the speed of machines to process, adapt, and scale.

But we need humans too, to match and outmatch the wits and ingenuity of the human attackers on the other side of that code. In short, we need teams of humans and machines, learning and informing each other—and working as one.

McAfee has fully embraced security analytic solutions using advanced, adaptive, and state-of-the-art machine learning, deep learning, and artificial intelligence techniques. Driving the pace of innovation, McAfee is moving quickly to evolve beyond the standard forms of advanced analytics to adopt a multi-layered approach known as “human-machine teaming.” This approach, by adding the human-in-the-loop within our products and processes, shows a 10x increase at catching threats with a 5-fold decrease in False Positives.*

Introduction to Artificial Intelligence and Machine Learning

Layers of artificial intelligence for each level of task

As humans have defined and refined advanced analytics, data scientists and technologists have recognized that there is an evolution of complexity towards more predictive and cognitive forms of computing. These levels, as depicted here, build one upon another toward the goal of better and faster intelligence.

Evolving machine learning for a better threat defense

Recent research highlights the need for machine learning for advanced detection capabilities. McAfee is evolving its machine learning cybersecurity technology to even more complex analytics called deep learning and artificial intelligence. Deep learning is the machine learning-based analytics approach that uses many layers of mathematical neurons—much like the human brain. It provides reasoning and a feed-forward or backward convolution of decision-making. Artificial intelligence adds complexity to deep learning, appending reasoning, suggested actions, and problem solving, often working in an n-dimensional space (like the brain). Machine learning, deep learning, and artificial intelligence are mathematically more complex as the computation becomes more brain- and human-like.

Each of these advanced machine learning applications in McAfee solutions consider:

Where the data will be gathered and computed, whether at the edge (i.e., “on premises” or “client”).

What raw data is needed and if sampling can be applied.

The cost of bandwidth and latency to the customer in time, budget, and resources, including people, hardware, and software.

Where the periodic or, preferably, continuous learning will occur.

Where, how, and when data will be stored.

How often the model should be recalculated due to changing customer processes, metadata, or governance policies.